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<li><a class="reference internal" href="#">Classification of text documents using sparse features</a><ul>
<li><a class="reference internal" href="#load-data-from-the-training-set">Load data from the training set</a></li>
<li><a class="reference internal" href="#benchmark-classifiers">Benchmark classifiers</a></li>
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  <div class="sphx-glr-download-link-note admonition note">
<p class="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-text-plot-document-classification-20newsgroups-py"><span class="std std-ref">here</span></a> to download the full example code or to run this example in your browser via Binder</p>
</div>
<div class="sphx-glr-example-title section" id="classification-of-text-documents-using-sparse-features">
<span id="sphx-glr-auto-examples-text-plot-document-classification-20newsgroups-py"></span><h1>Classification of text documents using sparse features<a class="headerlink" href="#classification-of-text-documents-using-sparse-features" title="Permalink to this headline">¶</a></h1>
<p>This is an example showing how scikit-learn can be used to classify documents
by topics using a bag-of-words approach. This example uses a scipy.sparse
matrix to store the features and demonstrates various classifiers that can
efficiently handle sparse matrices.</p>
<p>The dataset used in this example is the 20 newsgroups dataset. It will be
automatically downloaded, then cached.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Author: Peter Prettenhofer &lt;peter.prettenhofer@gmail.com&gt;</span>
<span class="c1">#         Olivier Grisel &lt;olivier.grisel@ensta.org&gt;</span>
<span class="c1">#         Mathieu Blondel &lt;mathieu@mblondel.org&gt;</span>
<span class="c1">#         Lars Buitinck</span>
<span class="c1"># License: BSD 3 clause</span>
<span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">optparse</span> <span class="kn">import</span> <span class="n">OptionParser</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>

<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_20newsgroups</span>
<span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="kn">import</span> <span class="n">TfidfVectorizer</span>
<span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="kn">import</span> <span class="n">HashingVectorizer</span>
<span class="kn">from</span> <span class="nn">sklearn.feature_selection</span> <span class="kn">import</span> <span class="n">SelectFromModel</span>
<span class="kn">from</span> <span class="nn">sklearn.feature_selection</span> <span class="kn">import</span> <span class="n">SelectKBest</span><span class="p">,</span> <span class="n">chi2</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">RidgeClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">LinearSVC</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">SGDClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Perceptron</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">PassiveAggressiveClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">BernoulliNB</span><span class="p">,</span> <span class="n">ComplementNB</span><span class="p">,</span> <span class="n">MultinomialNB</span>
<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">NearestCentroid</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.utils.extmath</span> <span class="kn">import</span> <span class="n">density</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>


<span class="c1"># Display progress logs on stdout</span>
<span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">,</span>
                    <span class="nb">format</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%(asctime)s</span><span class="s1"> </span><span class="si">%(levelname)s</span><span class="s1"> </span><span class="si">%(message)s</span><span class="s1">&#39;</span><span class="p">)</span>

<span class="n">op</span> <span class="o">=</span> <span class="n">OptionParser</span><span class="p">()</span>
<span class="n">op</span><span class="o">.</span><span class="n">add_option</span><span class="p">(</span><span class="s2">&quot;--report&quot;</span><span class="p">,</span>
              <span class="n">action</span><span class="o">=</span><span class="s2">&quot;store_true&quot;</span><span class="p">,</span> <span class="n">dest</span><span class="o">=</span><span class="s2">&quot;print_report&quot;</span><span class="p">,</span>
              <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Print a detailed classification report.&quot;</span><span class="p">)</span>
<span class="n">op</span><span class="o">.</span><span class="n">add_option</span><span class="p">(</span><span class="s2">&quot;--chi2_select&quot;</span><span class="p">,</span>
              <span class="n">action</span><span class="o">=</span><span class="s2">&quot;store&quot;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;int&quot;</span><span class="p">,</span> <span class="n">dest</span><span class="o">=</span><span class="s2">&quot;select_chi2&quot;</span><span class="p">,</span>
              <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Select some number of features using a chi-squared test&quot;</span><span class="p">)</span>
<span class="n">op</span><span class="o">.</span><span class="n">add_option</span><span class="p">(</span><span class="s2">&quot;--confusion_matrix&quot;</span><span class="p">,</span>
              <span class="n">action</span><span class="o">=</span><span class="s2">&quot;store_true&quot;</span><span class="p">,</span> <span class="n">dest</span><span class="o">=</span><span class="s2">&quot;print_cm&quot;</span><span class="p">,</span>
              <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Print the confusion matrix.&quot;</span><span class="p">)</span>
<span class="n">op</span><span class="o">.</span><span class="n">add_option</span><span class="p">(</span><span class="s2">&quot;--top10&quot;</span><span class="p">,</span>
              <span class="n">action</span><span class="o">=</span><span class="s2">&quot;store_true&quot;</span><span class="p">,</span> <span class="n">dest</span><span class="o">=</span><span class="s2">&quot;print_top10&quot;</span><span class="p">,</span>
              <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Print ten most discriminative terms per class&quot;</span>
                   <span class="s2">&quot; for every classifier.&quot;</span><span class="p">)</span>
<span class="n">op</span><span class="o">.</span><span class="n">add_option</span><span class="p">(</span><span class="s2">&quot;--all_categories&quot;</span><span class="p">,</span>
              <span class="n">action</span><span class="o">=</span><span class="s2">&quot;store_true&quot;</span><span class="p">,</span> <span class="n">dest</span><span class="o">=</span><span class="s2">&quot;all_categories&quot;</span><span class="p">,</span>
              <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Whether to use all categories or not.&quot;</span><span class="p">)</span>
<span class="n">op</span><span class="o">.</span><span class="n">add_option</span><span class="p">(</span><span class="s2">&quot;--use_hashing&quot;</span><span class="p">,</span>
              <span class="n">action</span><span class="o">=</span><span class="s2">&quot;store_true&quot;</span><span class="p">,</span>
              <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Use a hashing vectorizer.&quot;</span><span class="p">)</span>
<span class="n">op</span><span class="o">.</span><span class="n">add_option</span><span class="p">(</span><span class="s2">&quot;--n_features&quot;</span><span class="p">,</span>
              <span class="n">action</span><span class="o">=</span><span class="s2">&quot;store&quot;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">2</span> <span class="o">**</span> <span class="mi">16</span><span class="p">,</span>
              <span class="n">help</span><span class="o">=</span><span class="s2">&quot;n_features when using the hashing vectorizer.&quot;</span><span class="p">)</span>
<span class="n">op</span><span class="o">.</span><span class="n">add_option</span><span class="p">(</span><span class="s2">&quot;--filtered&quot;</span><span class="p">,</span>
              <span class="n">action</span><span class="o">=</span><span class="s2">&quot;store_true&quot;</span><span class="p">,</span>
              <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Remove newsgroup information that is easily overfit: &quot;</span>
                   <span class="s2">&quot;headers, signatures, and quoting.&quot;</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">is_interactive</span><span class="p">():</span>
    <span class="k">return</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">[</span><span class="s1">&#39;__main__&#39;</span><span class="p">],</span> <span class="s1">&#39;__file__&#39;</span><span class="p">)</span>


<span class="c1"># work-around for Jupyter notebook and IPython console</span>
<span class="n">argv</span> <span class="o">=</span> <span class="p">[]</span> <span class="k">if</span> <span class="n">is_interactive</span><span class="p">()</span> <span class="k">else</span> <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
<span class="p">(</span><span class="n">opts</span><span class="p">,</span> <span class="n">args</span><span class="p">)</span> <span class="o">=</span> <span class="n">op</span><span class="o">.</span><span class="n">parse_args</span><span class="p">(</span><span class="n">argv</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
    <span class="n">op</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s2">&quot;this script takes no arguments.&quot;</span><span class="p">)</span>
    <span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>
<span class="n">op</span><span class="o">.</span><span class="n">print_help</span><span class="p">()</span>
<span class="nb">print</span><span class="p">()</span>
</pre></div>
</div>
<div class="section" id="load-data-from-the-training-set">
<h2>Load data from the training set<a class="headerlink" href="#load-data-from-the-training-set" title="Permalink to this headline">¶</a></h2>
<p>Let’s load data from the newsgroups dataset which comprises around 18000
newsgroups posts on 20 topics split in two subsets: one for training (or
development) and the other one for testing (or for performance evaluation).</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">opts</span><span class="o">.</span><span class="n">all_categories</span><span class="p">:</span>
    <span class="n">categories</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">else</span><span class="p">:</span>
    <span class="n">categories</span> <span class="o">=</span> <span class="p">[</span>
        <span class="s1">&#39;alt.atheism&#39;</span><span class="p">,</span>
        <span class="s1">&#39;talk.religion.misc&#39;</span><span class="p">,</span>
        <span class="s1">&#39;comp.graphics&#39;</span><span class="p">,</span>
        <span class="s1">&#39;sci.space&#39;</span><span class="p">,</span>
    <span class="p">]</span>

<span class="k">if</span> <span class="n">opts</span><span class="o">.</span><span class="n">filtered</span><span class="p">:</span>
    <span class="n">remove</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;headers&#39;</span><span class="p">,</span> <span class="s1">&#39;footers&#39;</span><span class="p">,</span> <span class="s1">&#39;quotes&#39;</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
    <span class="n">remove</span> <span class="o">=</span> <span class="p">()</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Loading 20 newsgroups dataset for categories:&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">categories</span> <span class="k">if</span> <span class="n">categories</span> <span class="k">else</span> <span class="s2">&quot;all&quot;</span><span class="p">)</span>

<span class="n">data_train</span> <span class="o">=</span> <span class="n">fetch_20newsgroups</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">categories</span><span class="o">=</span><span class="n">categories</span><span class="p">,</span>
                                <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span>
                                <span class="n">remove</span><span class="o">=</span><span class="n">remove</span><span class="p">)</span>

<span class="n">data_test</span> <span class="o">=</span> <span class="n">fetch_20newsgroups</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="s1">&#39;test&#39;</span><span class="p">,</span> <span class="n">categories</span><span class="o">=</span><span class="n">categories</span><span class="p">,</span>
                               <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span>
                               <span class="n">remove</span><span class="o">=</span><span class="n">remove</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;data loaded&#39;</span><span class="p">)</span>

<span class="c1"># order of labels in `target_names` can be different from `categories`</span>
<span class="n">target_names</span> <span class="o">=</span> <span class="n">data_train</span><span class="o">.</span><span class="n">target_names</span>


<span class="k">def</span> <span class="nf">size_mb</span><span class="p">(</span><span class="n">docs</span><span class="p">):</span>
    <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">))</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">docs</span><span class="p">)</span> <span class="o">/</span> <span class="mf">1e6</span>


<span class="n">data_train_size_mb</span> <span class="o">=</span> <span class="n">size_mb</span><span class="p">(</span><span class="n">data_train</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
<span class="n">data_test_size_mb</span> <span class="o">=</span> <span class="n">size_mb</span><span class="p">(</span><span class="n">data_test</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%d</span><span class="s2"> documents - </span><span class="si">%0.3f</span><span class="s2">MB (training set)&quot;</span> <span class="o">%</span> <span class="p">(</span>
    <span class="nb">len</span><span class="p">(</span><span class="n">data_train</span><span class="o">.</span><span class="n">data</span><span class="p">),</span> <span class="n">data_train_size_mb</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%d</span><span class="s2"> documents - </span><span class="si">%0.3f</span><span class="s2">MB (test set)&quot;</span> <span class="o">%</span> <span class="p">(</span>
    <span class="nb">len</span><span class="p">(</span><span class="n">data_test</span><span class="o">.</span><span class="n">data</span><span class="p">),</span> <span class="n">data_test_size_mb</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%d</span><span class="s2"> categories&quot;</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">target_names</span><span class="p">))</span>
<span class="nb">print</span><span class="p">()</span>

<span class="c1"># split a training set and a test set</span>
<span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">data_train</span><span class="o">.</span><span class="n">target</span><span class="p">,</span> <span class="n">data_test</span><span class="o">.</span><span class="n">target</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Extracting features from the training data using a sparse vectorizer&quot;</span><span class="p">)</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="k">if</span> <span class="n">opts</span><span class="o">.</span><span class="n">use_hashing</span><span class="p">:</span>
    <span class="n">vectorizer</span> <span class="o">=</span> <span class="n">HashingVectorizer</span><span class="p">(</span><span class="n">stop_words</span><span class="o">=</span><span class="s1">&#39;english&#39;</span><span class="p">,</span> <span class="n">alternate_sign</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                                   <span class="n">n_features</span><span class="o">=</span><span class="n">opts</span><span class="o">.</span><span class="n">n_features</span><span class="p">)</span>
    <span class="n">X_train</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">data_train</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
    <span class="n">vectorizer</span> <span class="o">=</span> <span class="n">TfidfVectorizer</span><span class="p">(</span><span class="n">sublinear_tf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">max_df</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
                                 <span class="n">stop_words</span><span class="o">=</span><span class="s1">&#39;english&#39;</span><span class="p">)</span>
    <span class="n">X_train</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">data_train</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
<span class="n">duration</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;done in </span><span class="si">%f</span><span class="s2">s at </span><span class="si">%0.3f</span><span class="s2">MB/s&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">duration</span><span class="p">,</span> <span class="n">data_train_size_mb</span> <span class="o">/</span> <span class="n">duration</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;n_samples: </span><span class="si">%d</span><span class="s2">, n_features: </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Extracting features from the test data using the same vectorizer&quot;</span><span class="p">)</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">data_test</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
<span class="n">duration</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;done in </span><span class="si">%f</span><span class="s2">s at </span><span class="si">%0.3f</span><span class="s2">MB/s&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">duration</span><span class="p">,</span> <span class="n">data_test_size_mb</span> <span class="o">/</span> <span class="n">duration</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;n_samples: </span><span class="si">%d</span><span class="s2">, n_features: </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>

<span class="c1"># mapping from integer feature name to original token string</span>
<span class="k">if</span> <span class="n">opts</span><span class="o">.</span><span class="n">use_hashing</span><span class="p">:</span>
    <span class="n">feature_names</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">else</span><span class="p">:</span>
    <span class="n">feature_names</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">get_feature_names</span><span class="p">()</span>

<span class="k">if</span> <span class="n">opts</span><span class="o">.</span><span class="n">select_chi2</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Extracting </span><span class="si">%d</span><span class="s2"> best features by a chi-squared test&quot;</span> <span class="o">%</span>
          <span class="n">opts</span><span class="o">.</span><span class="n">select_chi2</span><span class="p">)</span>
    <span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
    <span class="n">ch2</span> <span class="o">=</span> <span class="n">SelectKBest</span><span class="p">(</span><span class="n">chi2</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="n">opts</span><span class="o">.</span><span class="n">select_chi2</span><span class="p">)</span>
    <span class="n">X_train</span> <span class="o">=</span> <span class="n">ch2</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
    <span class="n">X_test</span> <span class="o">=</span> <span class="n">ch2</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">feature_names</span><span class="p">:</span>
        <span class="c1"># keep selected feature names</span>
        <span class="n">feature_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">feature_names</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span>
                         <span class="ow">in</span> <span class="n">ch2</span><span class="o">.</span><span class="n">get_support</span><span class="p">(</span><span class="n">indices</span><span class="o">=</span><span class="kc">True</span><span class="p">)]</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;done in </span><span class="si">%f</span><span class="s2">s&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">()</span>

<span class="k">if</span> <span class="n">feature_names</span><span class="p">:</span>
    <span class="n">feature_names</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">feature_names</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">trim</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Trim string to fit on terminal (assuming 80-column display)&quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">s</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mi">80</span> <span class="k">else</span> <span class="n">s</span><span class="p">[:</span><span class="mi">77</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;...&quot;</span>
</pre></div>
</div>
</div>
<div class="section" id="benchmark-classifiers">
<h2>Benchmark classifiers<a class="headerlink" href="#benchmark-classifiers" title="Permalink to this headline">¶</a></h2>
<p>We train and test the datasets with 15 different classification models
and get performance results for each model.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">benchmark</span><span class="p">(</span><span class="n">clf</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;_&#39;</span> <span class="o">*</span> <span class="mi">80</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training: &quot;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">clf</span><span class="p">)</span>
    <span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
    <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
    <span class="n">train_time</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;train time: </span><span class="si">%0.3f</span><span class="s2">s&quot;</span> <span class="o">%</span> <span class="n">train_time</span><span class="p">)</span>

    <span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
    <span class="n">pred</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
    <span class="n">test_time</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;test time:  </span><span class="si">%0.3f</span><span class="s2">s&quot;</span> <span class="o">%</span> <span class="n">test_time</span><span class="p">)</span>

    <span class="n">score</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;accuracy:   </span><span class="si">%0.3f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">score</span><span class="p">)</span>

    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="s1">&#39;coef_&#39;</span><span class="p">):</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;dimensionality: </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">clf</span><span class="o">.</span><span class="n">coef_</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;density: </span><span class="si">%f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">density</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">coef_</span><span class="p">))</span>

        <span class="k">if</span> <span class="n">opts</span><span class="o">.</span><span class="n">print_top10</span> <span class="ow">and</span> <span class="n">feature_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;top 10 keywords per class:&quot;</span><span class="p">)</span>
            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">target_names</span><span class="p">):</span>
                <span class="n">top10</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">coef_</span><span class="p">[</span><span class="n">i</span><span class="p">])[</span><span class="o">-</span><span class="mi">10</span><span class="p">:]</span>
                <span class="nb">print</span><span class="p">(</span><span class="n">trim</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">: </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">feature_names</span><span class="p">[</span><span class="n">top10</span><span class="p">]))))</span>
        <span class="nb">print</span><span class="p">()</span>

    <span class="k">if</span> <span class="n">opts</span><span class="o">.</span><span class="n">print_report</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;classification report:&quot;</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred</span><span class="p">,</span>
                                            <span class="n">target_names</span><span class="o">=</span><span class="n">target_names</span><span class="p">))</span>

    <span class="k">if</span> <span class="n">opts</span><span class="o">.</span><span class="n">print_cm</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;confusion matrix:&quot;</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">()</span>
    <span class="n">clf_descr</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">clf</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;(&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
    <span class="k">return</span> <span class="n">clf_descr</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">train_time</span><span class="p">,</span> <span class="n">test_time</span>


<span class="n">results</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">clf</span><span class="p">,</span> <span class="n">name</span> <span class="ow">in</span> <span class="p">(</span>
        <span class="p">(</span><span class="n">RidgeClassifier</span><span class="p">(</span><span class="n">tol</span><span class="o">=</span><span class="mf">1e-2</span><span class="p">,</span> <span class="n">solver</span><span class="o">=</span><span class="s2">&quot;sag&quot;</span><span class="p">),</span> <span class="s2">&quot;Ridge Classifier&quot;</span><span class="p">),</span>
        <span class="p">(</span><span class="n">Perceptron</span><span class="p">(</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">50</span><span class="p">),</span> <span class="s2">&quot;Perceptron&quot;</span><span class="p">),</span>
        <span class="p">(</span><span class="n">PassiveAggressiveClassifier</span><span class="p">(</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">50</span><span class="p">),</span>
         <span class="s2">&quot;Passive-Aggressive&quot;</span><span class="p">),</span>
        <span class="p">(</span><span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">10</span><span class="p">),</span> <span class="s2">&quot;kNN&quot;</span><span class="p">),</span>
        <span class="p">(</span><span class="n">RandomForestClassifier</span><span class="p">(),</span> <span class="s2">&quot;Random forest&quot;</span><span class="p">)):</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;=&#39;</span> <span class="o">*</span> <span class="mi">80</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
    <span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">benchmark</span><span class="p">(</span><span class="n">clf</span><span class="p">))</span>

<span class="k">for</span> <span class="n">penalty</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;l2&quot;</span><span class="p">,</span> <span class="s2">&quot;l1&quot;</span><span class="p">]:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;=&#39;</span> <span class="o">*</span> <span class="mi">80</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> penalty&quot;</span> <span class="o">%</span> <span class="n">penalty</span><span class="o">.</span><span class="n">upper</span><span class="p">())</span>
    <span class="c1"># Train Liblinear model</span>
    <span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">benchmark</span><span class="p">(</span><span class="n">LinearSVC</span><span class="p">(</span><span class="n">penalty</span><span class="o">=</span><span class="n">penalty</span><span class="p">,</span> <span class="n">dual</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                                       <span class="n">tol</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">)))</span>

    <span class="c1"># Train SGD model</span>
    <span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">benchmark</span><span class="p">(</span><span class="n">SGDClassifier</span><span class="p">(</span><span class="n">alpha</span><span class="o">=.</span><span class="mi">0001</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
                                           <span class="n">penalty</span><span class="o">=</span><span class="n">penalty</span><span class="p">)))</span>

<span class="c1"># Train SGD with Elastic Net penalty</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;=&#39;</span> <span class="o">*</span> <span class="mi">80</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Elastic-Net penalty&quot;</span><span class="p">)</span>
<span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">benchmark</span><span class="p">(</span><span class="n">SGDClassifier</span><span class="p">(</span><span class="n">alpha</span><span class="o">=.</span><span class="mi">0001</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
                                       <span class="n">penalty</span><span class="o">=</span><span class="s2">&quot;elasticnet&quot;</span><span class="p">)))</span>

<span class="c1"># Train NearestCentroid without threshold</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;=&#39;</span> <span class="o">*</span> <span class="mi">80</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;NearestCentroid (aka Rocchio classifier)&quot;</span><span class="p">)</span>
<span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">benchmark</span><span class="p">(</span><span class="n">NearestCentroid</span><span class="p">()))</span>

<span class="c1"># Train sparse Naive Bayes classifiers</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;=&#39;</span> <span class="o">*</span> <span class="mi">80</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Naive Bayes&quot;</span><span class="p">)</span>
<span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">benchmark</span><span class="p">(</span><span class="n">MultinomialNB</span><span class="p">(</span><span class="n">alpha</span><span class="o">=.</span><span class="mi">01</span><span class="p">)))</span>
<span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">benchmark</span><span class="p">(</span><span class="n">BernoulliNB</span><span class="p">(</span><span class="n">alpha</span><span class="o">=.</span><span class="mi">01</span><span class="p">)))</span>
<span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">benchmark</span><span class="p">(</span><span class="n">ComplementNB</span><span class="p">(</span><span class="n">alpha</span><span class="o">=.</span><span class="mi">1</span><span class="p">)))</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;=&#39;</span> <span class="o">*</span> <span class="mi">80</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;LinearSVC with L1-based feature selection&quot;</span><span class="p">)</span>
<span class="c1"># The smaller C, the stronger the regularization.</span>
<span class="c1"># The more regularization, the more sparsity.</span>
<span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">benchmark</span><span class="p">(</span><span class="n">Pipeline</span><span class="p">([</span>
  <span class="p">(</span><span class="s1">&#39;feature_selection&#39;</span><span class="p">,</span> <span class="n">SelectFromModel</span><span class="p">(</span><span class="n">LinearSVC</span><span class="p">(</span><span class="n">penalty</span><span class="o">=</span><span class="s2">&quot;l1&quot;</span><span class="p">,</span> <span class="n">dual</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                                                  <span class="n">tol</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">))),</span>
  <span class="p">(</span><span class="s1">&#39;classification&#39;</span><span class="p">,</span> <span class="n">LinearSVC</span><span class="p">(</span><span class="n">penalty</span><span class="o">=</span><span class="s2">&quot;l2&quot;</span><span class="p">))])))</span>
</pre></div>
</div>
</div>
<div class="section" id="add-plots">
<h2>Add plots<a class="headerlink" href="#add-plots" title="Permalink to this headline">¶</a></h2>
<p>The bar plot indicates the accuracy, training time (normalized) and test time
(normalized) of each classifier.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">results</span><span class="p">))</span>

<span class="n">results</span> <span class="o">=</span> <span class="p">[[</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">results</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">)]</span>

<span class="n">clf_names</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">training_time</span><span class="p">,</span> <span class="n">test_time</span> <span class="o">=</span> <span class="n">results</span>
<span class="n">training_time</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">training_time</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">training_time</span><span class="p">)</span>
<span class="n">test_time</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">test_time</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">test_time</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Score&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="o">.</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;navy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="n">indices</span> <span class="o">+</span> <span class="o">.</span><span class="mi">3</span><span class="p">,</span> <span class="n">training_time</span><span class="p">,</span> <span class="o">.</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;training time&quot;</span><span class="p">,</span>
         <span class="n">color</span><span class="o">=</span><span class="s1">&#39;c&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="n">indices</span> <span class="o">+</span> <span class="o">.</span><span class="mi">6</span><span class="p">,</span> <span class="n">test_time</span><span class="p">,</span> <span class="o">.</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;test time&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;darkorange&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;best&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">left</span><span class="o">=.</span><span class="mi">25</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">top</span><span class="o">=.</span><span class="mi">95</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">bottom</span><span class="o">=.</span><span class="mi">05</span><span class="p">)</span>

<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">clf_names</span><span class="p">):</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="o">-.</span><span class="mi">3</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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